
# importing necessary libraries
%load_ext nb_black

import warnings

warnings.filterwarnings("ignore")
from statsmodels.tools.sm_exceptions import ConvergenceWarning

warnings.simplefilter("ignore", ConvergenceWarning)

# Libraries to help with reading and manipulating data
import pandas as pd
import numpy as np

# libaries to help with data visualization
import matplotlib.pyplot as plt
import seaborn as sns

# Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)
# setting the precision of floating numbers to 5 decimal points
pd.set_option("display.float_format", lambda x: "%.5f" % x)

# Library to split data
from sklearn.model_selection import train_test_split

# To build model for prediction
import statsmodels.stats.api as sms
from statsmodels.stats.outliers_influence import variance_inflation_factor
import statsmodels.api as sm
from statsmodels.tools.tools import add_constant
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree

# To tune different models
from sklearn.model_selection import GridSearchCV


# To get diferent metric scores
from sklearn.metrics import (
    f1_score,
    accuracy_score,
    recall_score,
    precision_score,
    confusion_matrix,
    roc_auc_score,
    plot_confusion_matrix,
    precision_recall_curve,
    roc_curve,
    make_scorer,
)


# import dataset
hotel = pd.read_csv("C:/Users/USER/Downloads/INNHotelsGroup.csv")


data = (
    hotel.copy()
)  # copy data into new variable to avoid any changes to the original copy


data.head()  # view first five rows of the dataset


data.tail()  # view last five rows of the dataset


data.shape  # the number of rows and columns in the dataset

(36275, 19)


data.info()  # concise summary of the columns of the dataset

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36275 entries, 0 to 36274
Data columns (total 19 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   Booking_ID                            36275 non-null  object 
 1   no_of_adults                          36275 non-null  int64  
 2   no_of_children                        36275 non-null  int64  
 3   no_of_weekend_nights                  36275 non-null  int64  
 4   no_of_week_nights                     36275 non-null  int64  
 5   type_of_meal_plan                     36275 non-null  object 
 6   required_car_parking_space            36275 non-null  int64  
 7   room_type_reserved                    36275 non-null  object 
 8   lead_time                             36275 non-null  int64  
 9   arrival_year                          36275 non-null  int64  
 10  arrival_month                         36275 non-null  int64  
 11  arrival_date                          36275 non-null  int64  
 12  market_segment_type                   36275 non-null  object 
 13  repeated_guest                        36275 non-null  int64  
 14  no_of_previous_cancellations          36275 non-null  int64  
 15  no_of_previous_bookings_not_canceled  36275 non-null  int64  
 16  avg_price_per_room                    36275 non-null  float64
 17  no_of_special_requests                36275 non-null  int64  
 18  booking_status                        36275 non-null  object 
dtypes: float64(1), int64(13), object(5)
memory usage: 5.3+ MB


data.duplicated().sum()  # Check for duplicate values

0


data.isnull().sum()  # check for missing values

Booking_ID                              0
no_of_adults                            0
no_of_children                          0
no_of_weekend_nights                    0
no_of_week_nights                       0
type_of_meal_plan                       0
required_car_parking_space              0
room_type_reserved                      0
lead_time                               0
arrival_year                            0
arrival_month                           0
arrival_date                            0
market_segment_type                     0
repeated_guest                          0
no_of_previous_cancellations            0
no_of_previous_bookings_not_canceled    0
avg_price_per_room                      0
no_of_special_requests                  0
booking_status                          0
dtype: int64


data.nunique()  # number of unique values in the columns of the dataset.

Booking_ID                              36275
no_of_adults                                5
no_of_children                              6
no_of_weekend_nights                        8
no_of_week_nights                          18
type_of_meal_plan                           4
required_car_parking_space                  2
room_type_reserved                          7
lead_time                                 352
arrival_year                                2
arrival_month                              12
arrival_date                               31
market_segment_type                         5
repeated_guest                              2
no_of_previous_cancellations                9
no_of_previous_bookings_not_canceled       59
avg_price_per_room                       3930
no_of_special_requests                      6
booking_status                              2
dtype: int64


data = data.drop(["Booking_ID"], axis=1)


data.describe().T  # statistical summary of the dataset


# Function needed to perform exploratory data analysis using histogram and boxplot


def histogram_boxplot(data, feature, figsize=(15, 10), kde=False, bins=None):
    """
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (15,10))
    kde: whether to show the density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a triangle will indicate the mean value of the column
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        data[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram


# Function needed to perform exploratory data analysis using histogram and boxplot


def labeled_barplot(data, feature, perc=False, n=None):
    """
    Barplot with percentage at the top

    data: dataframe
    feature: dataframe column
    perc: whether to display percentages instead of count (default is False)
    n: displays the top n category levels (default is None, i.e., display all levels)
    """

    total = len(data[feature])  # length of the column
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 2, 6))
    else:
        plt.figure(figsize=(n + 2, 6))

    plt.xticks(rotation=90, fontsize=15)
    ax = sns.countplot(
        data=data,
        x=feature,
        palette="Paired",
        order=data[feature].value_counts().index[:n],
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  # width of the plot
        y = p.get_height()  # height of the plot

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        )  # annotate the percentage

    plt.show()  # show the plot


### function to plot distributions wrt target


def distribution_plot_wrt_target(data, predictor, target):

    fig, axs = plt.subplots(2, 2, figsize=(12, 10))

    target_uniq = data[target].unique()

    axs[0, 0].set_title("Distribution of target for target=" + str(target_uniq[0]))
    sns.histplot(
        data=data[data[target] == target_uniq[0]],
        x=predictor,
        kde=True,
        ax=axs[0, 0],
        color="teal",
        stat="density",
    )

    axs[0, 1].set_title("Distribution of target for target=" + str(target_uniq[1]))
    sns.histplot(
        data=data[data[target] == target_uniq[1]],
        x=predictor,
        kde=True,
        ax=axs[0, 1],
        color="orange",
        stat="density",
    )

    axs[1, 0].set_title("Boxplot w.r.t target")
    sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0], palette="gist_rainbow")

    axs[1, 1].set_title("Boxplot (without outliers) w.r.t target")
    sns.boxplot(
        data=data,
        x=target,
        y=predictor,
        ax=axs[1, 1],
        showfliers=False,
        palette="gist_rainbow",
    )

    plt.tight_layout()
    plt.show()


def stacked_barplot(data, predictor, target):
    """
    Print the category counts and plot a stacked bar chart

    data: dataframe
    predictor: independent variable
    target: target variable
    """
    count = data[predictor].nunique()
    sorter = data[target].value_counts().index[-1]
    tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(
        by=sorter, ascending=False
    )
    print(tab1)
    print("-" * 120)
    tab = pd.crosstab(data[predictor], data[target], normalize="index").sort_values(
        by=sorter, ascending=False
    )
    tab.plot(kind="bar", stacked=True, figsize=(count + 5, 5))
    plt.legend(
        loc="lower left", frameon=False,
    )
    plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
    plt.show()


# Observation on number of adults
labeled_barplot(data, "no_of_adults", perc=True)


# Observation on number of children
labeled_barplot(data, "no_of_children", perc=True)


# replacing 9, and 10 children with 3
data["no_of_children"] = data["no_of_children"].replace([9, 10], 3)


# Observation on number of week nights
labeled_barplot(data, "no_of_week_nights", perc=True)


# Observation on number of weekend nights
labeled_barplot(data, "no_of_weekend_nights", perc=True)


# Observation on car parking space required
labeled_barplot(data, "required_car_parking_space", perc=True)


# Observation on type of meal plan
labeled_barplot(data, "type_of_meal_plan", perc=True)


# Observation on room type reserved
labeled_barplot(data, "room_type_reserved", perc=True)


# Observation on arrival month
labeled_barplot(data, "arrival_month", perc=True)


# Observation on market segment type
labeled_barplot(data, "market_segment_type", perc=True)


# Observation on number of special requests
labeled_barplot(data, "no_of_special_requests", perc=True)


# Observation on booking status
labeled_barplot(data, "booking_status", perc=True)


# encode canceled bookings to 1 and not canceled to 0
data["booking_status"] = data["booking_status"].apply(
    lambda x: 1 if x == "Canceled" else 0
)


histogram_boxplot(data, "lead_time")  # observation on lead time


histogram_boxplot(data, "avg_price_per_room")  # observation on average price per room


data[
    data["avg_price_per_room"] == 0
]  # filter rows with average price per room equal to zero


data.loc[
    data["avg_price_per_room"] == 0, "market_segment_type"
].value_counts()  # show the count of rows with average price per room equal to zero and market segment type

Complementary    354
Online           191
Name: market_segment_type, dtype: int64


# Calculating the 25th quantile
Q1 = data["avg_price_per_room"].quantile(
    0.25
)  # calculate 25th quantile for average price per room

# Calculating the 75th quantile
Q3 = data["avg_price_per_room"].quantile(
    0.75
)  # calculate 75th quantile for average price per room

# Calculating IQR
IQR = Q3 - Q1

# Calculating value of upper whisker
Upper_Whisker = Q3 + 1.5 * IQR

print("The value of the upper whisker is", Upper_Whisker)

The value of the upper whisker is 179.55


# assigning the outliers the value of upper whisker
data.loc[data["avg_price_per_room"] >= 500, "avg_price_per_room"] = Upper_Whisker


sns.boxplot(data=data, y="avg_price_per_room", x="market_segment_type")
# boxplot on average price and market segment

<AxesSubplot:xlabel='market_segment_type', ylabel='avg_price_per_room'>


histogram_boxplot(
    data, "no_of_previous_cancellations"
)  # observation on number of previous booking cancellations


histogram_boxplot(
    data, "no_of_previous_bookings_not_canceled"
)  # observation on number of previous bookings not canceled


cols_list = data.select_dtypes(
    include=np.number
).columns.tolist()  # create array of numerical type columns

plt.figure(figsize=(12, 7))  # size configurations of the plot

# Create heatmap of numerical columns
sns.heatmap(
    data[cols_list].corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral"
)
plt.show()
# show plot


stacked_barplot(
    data, "market_segment_type", "booking_status"
)  # bar graph of market segment type and booking status

booking_status           0      1    All
market_segment_type                     
All                  24390  11885  36275
Online               14739   8475  23214
Offline               7375   3153  10528
Corporate             1797    220   2017
Aviation                88     37    125
Complementary          391      0    391
------------------------------------------------------------------------------------------------------------------------


plt.figure(figsize=(10, 6))  # size configurations
sns.boxplot(
    data=data, x="booking_status", y="avg_price_per_room"
)  # create a boxplot of the two variables

<AxesSubplot:xlabel='booking_status', ylabel='avg_price_per_room'>


plt.figure(figsize=(10, 6))  # size configuration
sns.barplot(
    data=data, x="booking_status", y="no_of_special_requests"
)  # create a bar graph of the given variables

<AxesSubplot:xlabel='booking_status', ylabel='no_of_special_requests'>


plt.figure(figsize=(10, 6))  # size configuration
sns.barplot(
    data=data, x="no_of_special_requests", y="no_of_previous_cancellations",
)  # create a bar graph of given variables

<AxesSubplot:xlabel='no_of_special_requests', ylabel='no_of_previous_cancellations'>


plt.figure(figsize=(10, 6))  # size configurations
sns.barplot(
    data=data, x="no_of_special_requests", y="no_of_previous_bookings_not_canceled"
)  # create a bar graph of the given variables

<AxesSubplot:xlabel='no_of_special_requests', ylabel='no_of_previous_bookings_not_canceled'>


plt.figure(figsize=(10, 6))  # size configurations
sns.boxplot(
    data=data, x="no_of_special_requests", y="avg_price_per_room"
)  # create boxplot for no of special requests and average price per room (excluding the outliers)
plt.show()  # plot graph


distribution_plot_wrt_target(
    data, "avg_price_per_room", "booking_status"
)  # histogram and boxplot of average price per room and booking status


distribution_plot_wrt_target(
    data, "lead_time", "booking_status"
)  # histogram and boxplot of lead time and booking status


family_data = data[
    (data["no_of_children"] >= 0) & (data["no_of_adults"] > 1)
]  # create dataframe with number of children equal to 1 and number of adults greater than 1
family_data.shape  # show number of rows and columns of the filtered dataset

(28441, 18)


family_data["no_of_family_members"] = (
    family_data["no_of_adults"] + family_data["no_of_children"]
)  # create column with total number of adults and children


plt.figure(figsize=(10, 6))  # size configurations
sns.barplot(data=data, x="booking_status", y=family_data["no_of_family_members"])
# barplot of booking status and total family size


plt.figure(figsize=(10, 6))  # size configurations
sns.boxplot(data=data, x="booking_status", y=family_data["no_of_family_members"])
# boxplot of booking status and total family size


stay_data = data[
    (data["no_of_week_nights"] > 0) & (data["no_of_weekend_nights"] > 0)
]  # create dataframe with number of week nights greater than zero and number of weekend nights greater than 0.
stay_data.shape  # number of rows and columns of dataset

(17094, 18)


stay_data["total_days"] = (
    stay_data["no_of_week_nights"] + stay_data["no_of_weekend_nights"]
)  # create dataset with total number of days spent


plt.figure(figsize=(10, 6))  # size configurations
sns.barplot(data=data, x="booking_status", y=stay_data["total_days"])
# bar graph of booking status and total days spent


stay_data["no_of_week_nights"].sum()  # total number of bookings for weekdays

43443


stay_data["no_of_weekend_nights"].sum()  # total number of bookings for weekends

26313


labeled_barplot(
    data, "repeated_guest", perc=True
)  # bar graph of repeated guest feature


# grouping the data on arrival months and extracting the count of bookings
monthly_data = data.groupby(["arrival_month"])["booking_status"].count()

# creating a dataframe with months and count of customers in each month
monthly_data = pd.DataFrame(
    {"Month": list(monthly_data.index), "Guests": list(monthly_data.values)}
)

# plotting the trend over different months
plt.figure(figsize=(10, 5))  # size parameters
sns.lineplot(data=monthly_data, x="Month", y="Guests")
plt.show()  # show graph


canceled_bookings_per_month = data[
    (data["booking_status"] == 1)
]  # filter canceled bookings per month
canceled_bookings_per_month["arrival_month"].value_counts(
    normalize=True
)  # show relative count of results

10   0.15818
9    0.12941
8    0.12520
7    0.11056
6    0.10862
4    0.08372
5    0.07976
11   0.07362
3    0.05890
2    0.03618
12   0.03382
1    0.00202
Name: arrival_month, dtype: float64


labeled_barplot(
    canceled_bookings_per_month, "arrival_month", perc=True
)  # bar graph of arrival month


plt.figure(figsize=(12, 8))  # size parameters
sns.lineplot(
    data=data, x="arrival_month", y="avg_price_per_room"
)  # create lineplot between average price per room and arrival month
plt.show()  # show graph


plt.figure(figsize=(11, 8))  # size configurations
sns.boxplot(data=data, x="arrival_month", y="avg_price_per_room")
# boxplot of arrival month and average price per room


# outlier detection using boxplot
numeric_columns = data.select_dtypes(include=np.number).columns.tolist()
# dropping booking_status
numeric_columns.remove("booking_status")

plt.figure(figsize=(15, 12))  # size parameters

# generate outlier graphs for the various features
for i, variable in enumerate(numeric_columns):
    plt.subplot(4, 4, i + 1)
    plt.boxplot(data[variable], whis=1.5)
    plt.tight_layout()
    plt.title(variable)

plt.show()


# defining a function to compute different metrics to check performance of a classification model built using statsmodels
def model_performance_classification_statsmodels(
    model, predictors, target, threshold=0.5
):
    """
    Function to compute different metrics to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    threshold: threshold for classifying the observation as class 1
    """

    # checking which probabilities are greater than threshold
    pred_temp = model.predict(predictors) > threshold
    # rounding off the above values to get classes
    pred = np.round(pred_temp)

    acc = accuracy_score(target, pred)  # to compute Accuracy
    recall = recall_score(target, pred)  # to compute Recall
    precision = precision_score(target, pred)  # to compute Precision
    f1 = f1_score(target, pred)  # to compute F1-score

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
        index=[0],
    )

    return df_perf


# defining a function to plot the confusion_matrix of a classification model


def confusion_matrix_statsmodels(model, predictors, target, threshold=0.5):
    """
    To plot the confusion_matrix with percentages

    model: classifier
    predictors: independent variables
    target: dependent variable
    threshold: threshold for classifying the observation as class 1
    """
    y_pred = model.predict(predictors) > threshold
    cm = confusion_matrix(target, y_pred)
    labels = np.asarray(
        [
            ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
            for item in cm.flatten()
        ]
    ).reshape(2, 2)

    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")


X = data.drop(["booking_status"], axis=1)  # drop dependent variable
Y = data["booking_status"]  # assign dependent variable to variable Y

# adding constant
X = sm.add_constant(X)  # add constant to X

X = pd.get_dummies(X, drop_first=True)  # create dummies for X

# Splitting data in train and test sets
X_train, X_test, y_train, y_test = train_test_split(
    X, Y, test_size=0.30, random_state=1
)  # split the data into train test in the ratio 70:30 with random_state = 1
X.head()  # first five rows of the dataset


# print statements of the number of rows and columns in train and test data set and their relative percentages.
print("Shape of Training set : ", X_train.shape)
print("Shape of test set : ", X_test.shape)
print("Percentage of classes in training set:")
print(y_train.value_counts(normalize=True))
print("Percentage of classes in test set:")
print(y_test.value_counts(normalize=True))

Shape of Training set :  (25392, 28)
Shape of test set :  (10883, 28)
Percentage of classes in training set:
0   0.67064
1   0.32936
Name: booking_status, dtype: float64
Percentage of classes in test set:
0   0.67638
1   0.32362
Name: booking_status, dtype: float64


X_train.info()  # concise summary of dataframe

<class 'pandas.core.frame.DataFrame'>
Int64Index: 25392 entries, 13662 to 33003
Data columns (total 28 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   const                                 25392 non-null  float64
 1   no_of_adults                          25392 non-null  int64  
 2   no_of_children                        25392 non-null  int64  
 3   no_of_weekend_nights                  25392 non-null  int64  
 4   no_of_week_nights                     25392 non-null  int64  
 5   required_car_parking_space            25392 non-null  int64  
 6   lead_time                             25392 non-null  int64  
 7   arrival_year                          25392 non-null  int64  
 8   arrival_month                         25392 non-null  int64  
 9   arrival_date                          25392 non-null  int64  
 10  repeated_guest                        25392 non-null  int64  
 11  no_of_previous_cancellations          25392 non-null  int64  
 12  no_of_previous_bookings_not_canceled  25392 non-null  int64  
 13  avg_price_per_room                    25392 non-null  float64
 14  no_of_special_requests                25392 non-null  int64  
 15  type_of_meal_plan_Meal Plan 2         25392 non-null  uint8  
 16  type_of_meal_plan_Meal Plan 3         25392 non-null  uint8  
 17  type_of_meal_plan_Not Selected        25392 non-null  uint8  
 18  room_type_reserved_Room_Type 2        25392 non-null  uint8  
 19  room_type_reserved_Room_Type 3        25392 non-null  uint8  
 20  room_type_reserved_Room_Type 4        25392 non-null  uint8  
 21  room_type_reserved_Room_Type 5        25392 non-null  uint8  
 22  room_type_reserved_Room_Type 6        25392 non-null  uint8  
 23  room_type_reserved_Room_Type 7        25392 non-null  uint8  
 24  market_segment_type_Complementary     25392 non-null  uint8  
 25  market_segment_type_Corporate         25392 non-null  uint8  
 26  market_segment_type_Offline           25392 non-null  uint8  
 27  market_segment_type_Online            25392 non-null  uint8  
dtypes: float64(2), int64(13), uint8(13)
memory usage: 3.4 MB


# fitting logistic regression model
logit = sm.Logit(y_train, X_train.astype(float))
lg = logit.fit()  # fit logistic regression

print(lg.summary())  # print summary of the model

Warning: Maximum number of iterations has been exceeded.
         Current function value: 0.425090
         Iterations: 35
                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25392
Model:                          Logit   Df Residuals:                    25364
Method:                           MLE   Df Model:                           27
Date:                Fri, 27 Jan 2023   Pseudo R-squ.:                  0.3292
Time:                        15:39:33   Log-Likelihood:                -10794.
converged:                      False   LL-Null:                       -16091.
Covariance Type:            nonrobust   LLR p-value:                     0.000
========================================================================================================
                                           coef    std err          z      P>|z|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------
const                                 -922.8266    120.832     -7.637      0.000   -1159.653    -686.000
no_of_adults                             0.1137      0.038      3.019      0.003       0.040       0.188
no_of_children                           0.1580      0.062      2.544      0.011       0.036       0.280
no_of_weekend_nights                     0.1067      0.020      5.395      0.000       0.068       0.145
no_of_week_nights                        0.0397      0.012      3.235      0.001       0.016       0.064
required_car_parking_space              -1.5943      0.138    -11.565      0.000      -1.865      -1.324
lead_time                                0.0157      0.000     58.863      0.000       0.015       0.016
arrival_year                             0.4561      0.060      7.617      0.000       0.339       0.573
arrival_month                           -0.0417      0.006     -6.441      0.000      -0.054      -0.029
arrival_date                             0.0005      0.002      0.259      0.796      -0.003       0.004
repeated_guest                          -2.3472      0.617     -3.806      0.000      -3.556      -1.139
no_of_previous_cancellations             0.2664      0.086      3.108      0.002       0.098       0.434
no_of_previous_bookings_not_canceled    -0.1727      0.153     -1.131      0.258      -0.472       0.127
avg_price_per_room                       0.0188      0.001     25.396      0.000       0.017       0.020
no_of_special_requests                  -1.4689      0.030    -48.782      0.000      -1.528      -1.410
type_of_meal_plan_Meal Plan 2            0.1756      0.067      2.636      0.008       0.045       0.306
type_of_meal_plan_Meal Plan 3           17.3584   3987.836      0.004      0.997   -7798.656    7833.373
type_of_meal_plan_Not Selected           0.2784      0.053      5.247      0.000       0.174       0.382
room_type_reserved_Room_Type 2          -0.3605      0.131     -2.748      0.006      -0.618      -0.103
room_type_reserved_Room_Type 3          -0.0012      1.310     -0.001      0.999      -2.568       2.566
room_type_reserved_Room_Type 4          -0.2823      0.053     -5.304      0.000      -0.387      -0.178
room_type_reserved_Room_Type 5          -0.7189      0.209     -3.438      0.001      -1.129      -0.309
room_type_reserved_Room_Type 6          -0.9501      0.151     -6.274      0.000      -1.247      -0.653
room_type_reserved_Room_Type 7          -1.4003      0.294     -4.770      0.000      -1.976      -0.825
market_segment_type_Complementary      -40.5975   5.65e+05  -7.19e-05      1.000   -1.11e+06    1.11e+06
market_segment_type_Corporate           -1.1924      0.266     -4.483      0.000      -1.714      -0.671
market_segment_type_Offline             -2.1946      0.255     -8.621      0.000      -2.694      -1.696
market_segment_type_Online              -0.3995      0.251     -1.590      0.112      -0.892       0.093
========================================================================================================


print("Training performance:")  # print this statement
model_performance_classification_statsmodels(
    lg, X_train, y_train
)  # show performance of model on training data set

Training performance:


print("Training performance:")  # print statement
model_performance_classification_statsmodels(
    lg, X_test, y_test
)  # show performance of model on test data set

Training performance:


# create boxplot and histogram for numeric columns
for col in [
    "no_of_adults",
    "no_of_children",
    "no_of_weekend_nights",
    "no_of_week_nights",
    "lead_time",
    "no_of_previous_cancellations",
    "no_of_previous_bookings_not_canceled",
    "avg_price_per_room",
    "no_of_special_requests",
    "booking_status",
]:
    histogram_boxplot(data, col)  # create histogram and boxplot


for col in [
    "required_car_parking_space",
    "arrival_year",
    "arrival_month",
    "arrival_year",
    "repeated_guest",
    "type_of_meal_plan",
    "room_type_reserved",
    "market_segment_type",
]:
    labeled_barplot(data, col, perc=True)  # create bar graph for the above features


plt.figure(figsize=(12, 7))  # size parameters
sns.pairplot(data, hue="booking_status")
# pair plot for all variables in the dataset

<seaborn.axisgrid.PairGrid at 0x28654f6b9a0>

<Figure size 864x504 with 0 Axes>


# function to check VIF
def checking_vif(predictors):
    vif = pd.DataFrame()
    vif["feature"] = predictors.columns

    # calculating VIF for each feature
    vif["VIF"] = [
        variance_inflation_factor(predictors.values, i)
        for i in range(len(predictors.columns))
    ]
    return vif


checking_vif(X_train)  # display results of function


# initial list of columns
cols = X_train.columns.tolist()

# setting an initial max p-value
max_p_value = 1

while len(cols) > 0:
    # defining the train set
    x_train_aux = X_train[cols]

    # fitting the model
    model = sm.Logit(y_train, x_train_aux).fit(disp=False)

    # getting the p-values and the maximum p-value
    p_values = model.pvalues
    max_p_value = max(p_values)

    # name of the variable with maximum p-value
    feature_with_p_max = p_values.idxmax()

    if max_p_value > 0.05:
        cols.remove(feature_with_p_max)
    else:
        break

selected_features = cols
print(selected_features)  # print selected features to be used

['const', 'no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'required_car_parking_space', 'lead_time', 'arrival_year', 'arrival_month', 'repeated_guest', 'no_of_previous_cancellations', 'avg_price_per_room', 'no_of_special_requests', 'type_of_meal_plan_Meal Plan 2', 'type_of_meal_plan_Not Selected', 'room_type_reserved_Room_Type 2', 'room_type_reserved_Room_Type 4', 'room_type_reserved_Room_Type 5', 'room_type_reserved_Room_Type 6', 'room_type_reserved_Room_Type 7', 'market_segment_type_Corporate', 'market_segment_type_Offline']


X_train1 = X_train[selected_features]  # selected features on train data set
X_test1 = X_test[selected_features]  # selected features on test data set


logit1 = sm.Logit(
    y_train, X_train1.astype(float)
)  # train logistic regression on X_train1 and y_train
lg1 = logit1.fit(disp=False)  # fit logistic regression
print(lg1.summary())  # print summary of the model

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25392
Model:                          Logit   Df Residuals:                    25370
Method:                           MLE   Df Model:                           21
Date:                Fri, 27 Jan 2023   Pseudo R-squ.:                  0.3282
Time:                        15:45:21   Log-Likelihood:                -10810.
converged:                       True   LL-Null:                       -16091.
Covariance Type:            nonrobust   LLR p-value:                     0.000
==================================================================================================
                                     coef    std err          z      P>|z|      [0.025      0.975]
--------------------------------------------------------------------------------------------------
const                           -915.6391    120.471     -7.600      0.000   -1151.758    -679.520
no_of_adults                       0.1088      0.037      2.914      0.004       0.036       0.182
no_of_children                     0.1531      0.062      2.470      0.014       0.032       0.275
no_of_weekend_nights               0.1086      0.020      5.498      0.000       0.070       0.147
no_of_week_nights                  0.0417      0.012      3.399      0.001       0.018       0.066
required_car_parking_space        -1.5947      0.138    -11.564      0.000      -1.865      -1.324
lead_time                          0.0157      0.000     59.213      0.000       0.015       0.016
arrival_year                       0.4523      0.060      7.576      0.000       0.335       0.569
arrival_month                     -0.0425      0.006     -6.591      0.000      -0.055      -0.030
repeated_guest                    -2.7367      0.557     -4.916      0.000      -3.828      -1.646
no_of_previous_cancellations       0.2288      0.077      2.983      0.003       0.078       0.379
avg_price_per_room                 0.0192      0.001     26.336      0.000       0.018       0.021
no_of_special_requests            -1.4698      0.030    -48.884      0.000      -1.529      -1.411
type_of_meal_plan_Meal Plan 2      0.1642      0.067      2.469      0.014       0.034       0.295
type_of_meal_plan_Not Selected     0.2860      0.053      5.406      0.000       0.182       0.390
room_type_reserved_Room_Type 2    -0.3552      0.131     -2.709      0.007      -0.612      -0.098
room_type_reserved_Room_Type 4    -0.2828      0.053     -5.330      0.000      -0.387      -0.179
room_type_reserved_Room_Type 5    -0.7364      0.208     -3.535      0.000      -1.145      -0.328
room_type_reserved_Room_Type 6    -0.9682      0.151     -6.403      0.000      -1.265      -0.672
room_type_reserved_Room_Type 7    -1.4343      0.293     -4.892      0.000      -2.009      -0.860
market_segment_type_Corporate     -0.7913      0.103     -7.692      0.000      -0.993      -0.590
market_segment_type_Offline       -1.7854      0.052    -34.363      0.000      -1.887      -1.684
==================================================================================================


print("Training performance:")  # print statement
model_performance_classification_statsmodels(
    lg1, X_train1, y_train
)  # check performance on X_train1 and y_train

Training performance:


# converting coefficients to odds
odds = np.exp(lg1.params)

# finding the percentage change
perc_change_odds = (np.exp(lg1.params) - 1) * 100

# removing limit from number of columns to display
pd.set_option("display.max_columns", None)

# adding the odds to a dataframe
pd.DataFrame({"Odds": odds, "Change_odd%": perc_change_odds}, index=X_train1.columns)


# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_train1, y_train)


print("Training performance:")
log_reg_model_train_perf = model_performance_classification_statsmodels(
    lg1, X_train1, y_train
)  # performance on X_train1 and y_train
log_reg_model_train_perf

Training performance:


# parameters to plot roc-auc graph
logit_roc_auc_train = roc_auc_score(y_train, lg1.predict(X_train1))
fpr, tpr, thresholds = roc_curve(y_train, lg1.predict(X_train1))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.01])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()


# Optimal threshold as per AUC-ROC curve
# The optimal cut off would be where tpr is high and fpr is low
fpr, tpr, thresholds = roc_curve(y_train, lg1.predict(X_train1))

optimal_idx = np.argmax(tpr - fpr)
optimal_threshold_auc_roc = thresholds[optimal_idx]
print(optimal_threshold_auc_roc)  # show optimal threshold value

0.3700522558707844


# creating confusion matrix
confusion_matrix_statsmodels(
    lg1, X_train1, y_train, threshold=optimal_threshold_auc_roc
)  # confusion matrix for X_train1 and y_train with optimal_threshold_auc_roc as threshold


# checking model performance for this model
log_reg_model_train_perf_threshold_auc_roc = model_performance_classification_statsmodels(
    lg1, X_train1, y_train, threshold=optimal_threshold_auc_roc
)
print("Training performance:")
log_reg_model_train_perf_threshold_auc_roc  # performance on train data set

Training performance:


y_scores = lg1.predict(X_train1)
prec, rec, tre = precision_recall_curve(
    y_train, y_scores,
)  # axes for precision-recall curve

# parameters to draw curve
def plot_prec_recall_vs_tresh(precisions, recalls, thresholds):
    plt.plot(thresholds, precisions[:-1], "b--", label="precision")
    plt.plot(thresholds, recalls[:-1], "g--", label="recall")
    plt.xlabel("Threshold")
    plt.legend(loc="upper left")
    plt.ylim([0, 1])


plt.figure(figsize=(10, 7))
plot_prec_recall_vs_tresh(prec, rec, tre)
plt.show()


# setting the threshold
optimal_threshold_curve = 0.42


# creating confusion matrix
confusion_matrix_statsmodels(
    lg1, X_train1, y_train, threshold=optimal_threshold_curve,
)  # confusion matrix for X_train1 and y_train with optimal_threshold_curve as threshold


log_reg_model_train_perf_threshold_curve = model_performance_classification_statsmodels(
    lg1, X_train1, y_train, threshold=optimal_threshold_curve
)
print("Training performance:")
log_reg_model_train_perf_threshold_curve  # performance on train data set

Training performance:


# training performance comparison

models_train_comp_df = pd.concat(
    [
        log_reg_model_train_perf.T,
        log_reg_model_train_perf_threshold_auc_roc.T,
        log_reg_model_train_perf_threshold_curve.T,
    ],
    axis=1,
)
models_train_comp_df.columns = [
    "Logistic Regression sklearn",
    "Logistic Regression-0.38 Threshold",
    "Logistic Regression-0.42 Threshold",
]

print("Training performance comparison:")
models_train_comp_df

Training performance comparison:


# creating confusion matrix
confusion_matrix_statsmodels(
    lg1, X_test1, y_test
)  # Complete the code to create confusion matrix for X_test1 and y_test


log_reg_model_test_perf = model_performance_classification_statsmodels(
    lg1, X_test1, y_test, threshold=optimal_threshold_auc_roc
)  # check performance on X_test1 and y_test

print("Test performance:")  # print statement
log_reg_model_test_perf  # performance on test data

Test performance:


# parameters to draw curve
logit_roc_auc_train = roc_auc_score(y_test, lg1.predict(X_test1))
fpr, tpr, thresholds = roc_curve(y_test, lg1.predict(X_test1))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.01])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()


# creating confusion matrix
confusion_matrix_statsmodels(
    lg1, X_test1, y_test, threshold=optimal_threshold_auc_roc
)  # create confusion matrix for X_test1 and y_test using optimal_threshold_auc_roc as threshold


# checking model performance for this model
log_reg_model_test_perf_threshold_auc_roc = model_performance_classification_statsmodels(
    lg1, X_test1, y_test, threshold=optimal_threshold_auc_roc
)
print("Test performance:")
log_reg_model_test_perf_threshold_auc_roc  # performance on test data

Test performance:


# creating confusion matrix
confusion_matrix_statsmodels(
    lg1, X_test1, y_test, threshold=optimal_threshold_curve
)  # create confusion matrix for X_test1 and y_test using optimal_threshold_curve as threshold


log_reg_model_test_perf_threshold_curve = model_performance_classification_statsmodels(
    lg1, X_test1, y_test, threshold=optimal_threshold_curve
)
print("Test performance:")
log_reg_model_test_perf_threshold_curve  # performance on test data

Test performance:


# training performance comparison

models_train_comp_df = pd.concat(
    [
        log_reg_model_train_perf.T,
        log_reg_model_train_perf_threshold_auc_roc.T,
        log_reg_model_train_perf_threshold_curve.T,
    ],
    axis=1,
)
models_train_comp_df.columns = [
    "Logistic Regression-default Threshold",
    "Logistic Regression-0.37 Threshold",
    "Logistic Regression-0.42 Threshold",
]

print("Training performance comparison:")
models_train_comp_df

Training performance comparison:


# testing performance comparison

models_test_comp_df = pd.concat(
    [
        log_reg_model_test_perf.T,
        log_reg_model_test_perf_threshold_auc_roc.T,
        log_reg_model_test_perf_threshold_curve.T,
    ],
    axis=1,
)
models_test_comp_df.columns = [
    "Logistic Regression-default Threshold",
    "Logistic Regression-0.37 Threshold",
    "Logistic Regression-0.42 Threshold",
]

print("Testing performance comparison:")
models_test_comp_df

Testing performance comparison:


X = data.drop(["booking_status"], axis=1)  # drop dependent variable from dataset
Y = data["booking_status"]  # assign variable Y to dependent variable

X = pd.get_dummies(X, drop_first=True)  # create dummies for X

# Splitting data in train and test sets
X_train, X_test, y_train, y_test = train_test_split(
    X, Y, test_size=0.30, random_state=1
)  # split the data into train test in the ratio 70:30 with random_state = 1


print(
    "Shape of Training set : ", X_train.shape
)  # number of rows and columns of training set
print("Shape of test set : ", X_test.shape)  # number of rows and columns of testing set
print("Percentage of classes in training set:")  # print statement
print(y_train.value_counts(normalize=True))  # ratio of both classes in training set
print("Percentage of classes in test set:")  # print statement
print(y_test.value_counts(normalize=True))  # ratio of both classes in test set

Shape of Training set :  (25392, 27)
Shape of test set :  (10883, 27)
Percentage of classes in training set:
0   0.67064
1   0.32936
Name: booking_status, dtype: float64
Percentage of classes in test set:
0   0.67638
1   0.32362
Name: booking_status, dtype: float64


X_train.head()  # first five rows of training data set


# defining a function to compute different metrics to check performance of a classification model built using sklearn
def model_performance_classification_sklearn(model, predictors, target):
    """
    Function to compute different metrics to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    """

    # predicting using the independent variables
    pred = model.predict(predictors)

    acc = accuracy_score(target, pred)  # to compute Accuracy
    recall = recall_score(target, pred)  # to compute Recall
    precision = precision_score(target, pred)  # to compute Precision
    f1 = f1_score(target, pred)  # to compute F1-score

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
        index=[0],
    )

    return df_perf


def confusion_matrix_sklearn(model, predictors, target):
    """
    To plot the confusion_matrix with percentages

    model: classifier
    predictors: independent variables
    target: dependent variable
    """
    y_pred = model.predict(predictors)
    cm = confusion_matrix(target, y_pred)
    labels = np.asarray(
        [
            ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
            for item in cm.flatten()
        ]
    ).reshape(2, 2)

    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")


model = DecisionTreeClassifier(random_state=1)  # create decision tree
model.fit(X_train, y_train)  ## fit decision tree on train data

DecisionTreeClassifier(random_state=1)


confusion_matrix_sklearn(model, X_train, y_train)  # create confusion matrix


decision_tree_perf_train_without = model_performance_classification_sklearn(
    model, X_train, y_train
)  # performance of decison tree on training data
decision_tree_perf_train_without


confusion_matrix_sklearn(model, X_test, y_test)  # create confusion matrix


decision_tree_perf_test_without = model_performance_classification_sklearn(
    model, X_test, y_test
)  # performance of decison tree on test data
decision_tree_perf_test_without


model = DecisionTreeClassifier(random_state=1, class_weight="balanced")
model.fit(X_train, y_train)  # fit model on train data using the above set parameters

DecisionTreeClassifier(class_weight='balanced', random_state=1)


confusion_matrix_sklearn(model, X_train, y_train)


decision_tree_perf_train = model_performance_classification_sklearn(
    model, X_train, y_train  # performance on train data
)
decision_tree_perf_train


confusion_matrix_sklearn(model, X_test, y_test)  # create confusion matrix


decision_tree_perf_test = model_performance_classification_sklearn(
    model, X_test, y_test  # performance on test data
)
decision_tree_perf_test


feature_names = list(X_train.columns)  # train data columns
importances = model.feature_importances_  # show their relative importances
indices = np.argsort(importances)

# parameters to draw graph
plt.figure(figsize=(8, 8))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()


# Choose the type of classifier.
estimator = DecisionTreeClassifier(random_state=1, class_weight="balanced")

# Grid of parameters to choose from
parameters = {
    "max_depth": np.arange(2, 7, 2),
    "max_leaf_nodes": [50, 75, 150, 250],
    "min_samples_split": [10, 30, 50, 70],
}

# Type of scoring used to compare parameter combinations
acc_scorer = make_scorer(f1_score)

# Run the grid search
grid_obj = GridSearchCV(estimator, parameters, scoring=acc_scorer, cv=5)
grid_obj = grid_obj.fit(X_train, y_train)

# Set the clf to the best combination of parameters
estimator = grid_obj.best_estimator_

# Fit the best algorithm to the data.
estimator.fit(X_train, y_train)

DecisionTreeClassifier(class_weight='balanced', max_depth=6, max_leaf_nodes=50,
                       min_samples_split=10, random_state=1)


confusion_matrix_sklearn(estimator, X_train, y_train)  # create confusion matrix


decision_tree_tune_perf_train = model_performance_classification_sklearn(
    estimator, X_train, y_train
)  # performance on train set
decision_tree_tune_perf_train


confusion_matrix_sklearn(estimator, X_test, y_test)  # create confusion matrix


decision_tree_tune_perf_test = model_performance_classification_sklearn(
    estimator, X_test, y_test
)  # performance on test set
decision_tree_tune_perf_test


feature_names = list(X_train.columns)  # train data columns
importances = estimator.feature_importances_
indices = np.argsort(importances)


# parameters to draw decision tree
plt.figure(figsize=(20, 10))
out = tree.plot_tree(
    estimator,
    feature_names=feature_names,
    filled=True,
    fontsize=9,
    node_ids=False,
    class_names=None,
)
# below code will add arrows to the decision tree split if they are missing
for o in out:
    arrow = o.arrow_patch
    if arrow is not None:
        arrow.set_edgecolor("black")
        arrow.set_linewidth(1)
plt.show()


# Text report showing the rules of a decision tree -
print(tree.export_text(estimator, feature_names=feature_names, show_weights=True))

|--- lead_time <= 151.50
|   |--- no_of_special_requests <= 0.50
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |--- avg_price_per_room <= 196.50
|   |   |   |   |   |   |--- weights: [1736.39, 133.59] class: 0
|   |   |   |   |   |--- avg_price_per_room >  196.50
|   |   |   |   |   |   |--- weights: [0.75, 24.29] class: 1
|   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |--- lead_time <= 68.50
|   |   |   |   |   |   |--- weights: [960.27, 223.16] class: 0
|   |   |   |   |   |--- lead_time >  68.50
|   |   |   |   |   |   |--- weights: [129.73, 160.92] class: 1
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- lead_time <= 117.50
|   |   |   |   |   |--- avg_price_per_room <= 93.58
|   |   |   |   |   |   |--- weights: [214.72, 227.72] class: 1
|   |   |   |   |   |--- avg_price_per_room >  93.58
|   |   |   |   |   |   |--- weights: [82.76, 285.41] class: 1
|   |   |   |   |--- lead_time >  117.50
|   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |--- weights: [87.23, 81.98] class: 0
|   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |--- weights: [228.14, 48.58] class: 0
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- lead_time <= 13.50
|   |   |   |   |--- avg_price_per_room <= 99.44
|   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |--- weights: [92.45, 0.00] class: 0
|   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |--- weights: [363.83, 132.08] class: 0
|   |   |   |   |--- avg_price_per_room >  99.44
|   |   |   |   |   |--- lead_time <= 3.50
|   |   |   |   |   |   |--- weights: [219.94, 85.01] class: 0
|   |   |   |   |   |--- lead_time >  3.50
|   |   |   |   |   |   |--- weights: [132.71, 280.85] class: 1
|   |   |   |--- lead_time >  13.50
|   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |--- avg_price_per_room <= 71.92
|   |   |   |   |   |   |--- weights: [158.80, 159.40] class: 1
|   |   |   |   |   |--- avg_price_per_room >  71.92
|   |   |   |   |   |   |--- weights: [850.67, 3543.28] class: 1
|   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |--- weights: [48.46, 1.52] class: 0
|   |--- no_of_special_requests >  0.50
|   |   |--- no_of_special_requests <= 1.50
|   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |--- lead_time <= 102.50
|   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |--- weights: [697.09, 9.11] class: 0
|   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |--- weights: [15.66, 9.11] class: 0
|   |   |   |   |--- lead_time >  102.50
|   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |--- weights: [32.06, 19.74] class: 0
|   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |--- weights: [44.73, 3.04] class: 0
|   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |--- lead_time <= 8.50
|   |   |   |   |   |--- lead_time <= 4.50
|   |   |   |   |   |   |--- weights: [498.03, 44.03] class: 0
|   |   |   |   |   |--- lead_time >  4.50
|   |   |   |   |   |   |--- weights: [258.71, 63.76] class: 0
|   |   |   |   |--- lead_time >  8.50
|   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |--- weights: [2512.51, 1451.32] class: 0
|   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |--- weights: [134.20, 1.52] class: 0
|   |   |--- no_of_special_requests >  1.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |--- weights: [1585.04, 0.00] class: 0
|   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |--- weights: [180.42, 57.69] class: 0
|   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |--- weights: [52.19, 0.00] class: 0
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |--- weights: [184.90, 56.17] class: 0
|   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |--- weights: [106.61, 106.27] class: 0
|   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |--- weights: [67.10, 0.00] class: 0
|--- lead_time >  151.50
|   |--- avg_price_per_room <= 100.04
|   |   |--- no_of_special_requests <= 0.50
|   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |--- lead_time <= 163.50
|   |   |   |   |   |   |--- weights: [3.73, 24.29] class: 1
|   |   |   |   |   |--- lead_time >  163.50
|   |   |   |   |   |   |--- weights: [257.96, 62.24] class: 0
|   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |--- avg_price_per_room <= 2.50
|   |   |   |   |   |   |--- weights: [8.95, 3.04] class: 0
|   |   |   |   |   |--- avg_price_per_room >  2.50
|   |   |   |   |   |   |--- weights: [0.75, 97.16] class: 1
|   |   |   |--- no_of_adults >  1.50
|   |   |   |   |--- avg_price_per_room <= 82.47
|   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |--- weights: [2.98, 282.37] class: 1
|   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |--- weights: [213.97, 385.60] class: 1
|   |   |   |   |--- avg_price_per_room >  82.47
|   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |--- weights: [23.86, 1030.80] class: 1
|   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |--- weights: [5.22, 0.00] class: 0
|   |   |--- no_of_special_requests >  0.50
|   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |--- lead_time <= 180.50
|   |   |   |   |   |--- lead_time <= 159.50
|   |   |   |   |   |   |--- weights: [7.46, 7.59] class: 1
|   |   |   |   |   |--- lead_time >  159.50
|   |   |   |   |   |   |--- weights: [37.28, 4.55] class: 0
|   |   |   |   |--- lead_time >  180.50
|   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |--- weights: [20.13, 212.54] class: 1
|   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |--- weights: [8.95, 0.00] class: 0
|   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |--- weights: [231.12, 110.82] class: 0
|   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |--- weights: [19.38, 34.92] class: 1
|   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |--- lead_time <= 348.50
|   |   |   |   |   |   |--- weights: [106.61, 3.04] class: 0
|   |   |   |   |   |--- lead_time >  348.50
|   |   |   |   |   |   |--- weights: [5.96, 4.55] class: 0
|   |--- avg_price_per_room >  100.04
|   |   |--- arrival_month <= 11.50
|   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |--- weights: [0.00, 3200.19] class: 1
|   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |--- weights: [23.11, 0.00] class: 0
|   |   |--- arrival_month >  11.50
|   |   |   |--- no_of_special_requests <= 0.50
|   |   |   |   |--- weights: [35.04, 0.00] class: 0
|   |   |   |--- no_of_special_requests >  0.50
|   |   |   |   |--- arrival_date <= 24.50
|   |   |   |   |   |--- weights: [3.73, 0.00] class: 0
|   |   |   |   |--- arrival_date >  24.50
|   |   |   |   |   |--- weights: [3.73, 22.77] class: 1


# importance of features in the tree building

importances = estimator.feature_importances_
indices = np.argsort(importances)

# graph parameters
plt.figure(figsize=(8, 8))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()


clf = DecisionTreeClassifier(
    random_state=1, class_weight="balanced"
)  # parameters for setting the cost complexity path
path = clf.cost_complexity_pruning_path(
    X_train, y_train
)  # cost complexity on train data
ccp_alphas, impurities = abs(path.ccp_alphas), path.impurities


pd.DataFrame(path)  # output of cost complexity computation


# parameters to draw graph for total impurity vs effective alpha for training data
fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(ccp_alphas[:-1], impurities[:-1], marker="o", drawstyle="steps-post")
ax.set_xlabel("effective alpha")
ax.set_ylabel("total impurity of leaves")
ax.set_title("Total Impurity vs effective alpha for training set")
plt.show()


clfs = []
for ccp_alpha in ccp_alphas:
    clf = DecisionTreeClassifier(
        random_state=1, ccp_alpha=ccp_alpha, class_weight="balanced"
    )
    clf.fit(X_train, y_train)  # fit decision tree on training data
    clfs.append(clf)
print(
    "Number of nodes in the last tree is: {} with ccp_alpha: {}".format(
        clfs[-1].tree_.node_count, ccp_alphas[-1]
    )
)

Number of nodes in the last tree is: 1 with ccp_alpha: 0.0811791438913696


clfs = clfs[:-1]
ccp_alphas = ccp_alphas[:-1]

node_counts = [clf.tree_.node_count for clf in clfs]
depth = [clf.tree_.max_depth for clf in clfs]
fig, ax = plt.subplots(2, 1, figsize=(10, 7))
ax[0].plot(ccp_alphas, node_counts, marker="o", drawstyle="steps-post")
ax[0].set_xlabel("alpha")
ax[0].set_ylabel("number of nodes")
ax[0].set_title("Number of nodes vs alpha")
ax[1].plot(ccp_alphas, depth, marker="o", drawstyle="steps-post")
ax[1].set_xlabel("alpha")
ax[1].set_ylabel("depth of tree")
ax[1].set_title("Depth vs alpha")
fig.tight_layout()


f1_train = []
for clf in clfs:
    pred_train = clf.predict(X_train)
    values_train = f1_score(y_train, pred_train)
    f1_train.append(values_train)

f1_test = []
for clf in clfs:
    pred_test = clf.predict(X_test)
    values_test = f1_score(y_test, pred_test)
    f1_test.append(values_test)


fig, ax = plt.subplots(figsize=(15, 5))  # size configurations
ax.set_xlabel("alpha")  # label of x-axis
ax.set_ylabel("F1 Score")  # label of y axis
ax.set_title("F1 Score vs alpha for training and testing sets")  # title of plot
ax.plot(
    ccp_alphas, f1_train, marker="o", label="train", drawstyle="steps-post"
)  # parameters for plot on train data
ax.plot(
    ccp_alphas, f1_test, marker="o", label="test", drawstyle="steps-post"
)  # parameters for plot on test data
ax.legend()  # plot legend in the graph
plt.show()  # show graph


# Create model to obtain the highest train and test recall
index_best_model = np.argmax(f1_test)
best_model = clfs[index_best_model]
print(best_model)

DecisionTreeClassifier(ccp_alpha=0.00012267633155167043,
                       class_weight='balanced', random_state=1)


confusion_matrix_sklearn(best_model, X_train, y_train)  # create confusion matrix


decision_tree_post_perf_train = model_performance_classification_sklearn(
    best_model, X_train, y_train  # performance of decision tree on train data
)
decision_tree_post_perf_train  # show results


confusion_matrix_sklearn(best_model, X_test, y_test)  # create confusion matrix


decision_tree_post_test = model_performance_classification_sklearn(
    best_model, X_test, y_test
)  # performance of decision tree on test data
decision_tree_post_test  # show results after the pruning


feature_names = list(X_train.columns)
importances = estimator.feature_importances_
indices = np.argsort(importances)


# parameters to show decision tree diagram
plt.figure(figsize=(20, 10))
out = tree.plot_tree(
    estimator,
    feature_names=feature_names,
    filled=True,
    fontsize=9,
    node_ids=False,
    class_names=None,
)
# below code will add arrows to the decision tree split if they are missing
for o in out:
    arrow = o.arrow_patch
    if arrow is not None:
        arrow.set_edgecolor("black")
        arrow.set_linewidth(1)
plt.show()


# Text report showing the rules of a decision tree -
print(tree.export_text(estimator, feature_names=feature_names, show_weights=True))

|--- lead_time <= 151.50
|   |--- no_of_special_requests <= 0.50
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |--- avg_price_per_room <= 196.50
|   |   |   |   |   |   |--- weights: [1736.39, 133.59] class: 0
|   |   |   |   |   |--- avg_price_per_room >  196.50
|   |   |   |   |   |   |--- weights: [0.75, 24.29] class: 1
|   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |--- lead_time <= 68.50
|   |   |   |   |   |   |--- weights: [960.27, 223.16] class: 0
|   |   |   |   |   |--- lead_time >  68.50
|   |   |   |   |   |   |--- weights: [129.73, 160.92] class: 1
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- lead_time <= 117.50
|   |   |   |   |   |--- avg_price_per_room <= 93.58
|   |   |   |   |   |   |--- weights: [214.72, 227.72] class: 1
|   |   |   |   |   |--- avg_price_per_room >  93.58
|   |   |   |   |   |   |--- weights: [82.76, 285.41] class: 1
|   |   |   |   |--- lead_time >  117.50
|   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |--- weights: [87.23, 81.98] class: 0
|   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |--- weights: [228.14, 48.58] class: 0
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- lead_time <= 13.50
|   |   |   |   |--- avg_price_per_room <= 99.44
|   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |--- weights: [92.45, 0.00] class: 0
|   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |--- weights: [363.83, 132.08] class: 0
|   |   |   |   |--- avg_price_per_room >  99.44
|   |   |   |   |   |--- lead_time <= 3.50
|   |   |   |   |   |   |--- weights: [219.94, 85.01] class: 0
|   |   |   |   |   |--- lead_time >  3.50
|   |   |   |   |   |   |--- weights: [132.71, 280.85] class: 1
|   |   |   |--- lead_time >  13.50
|   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |--- avg_price_per_room <= 71.92
|   |   |   |   |   |   |--- weights: [158.80, 159.40] class: 1
|   |   |   |   |   |--- avg_price_per_room >  71.92
|   |   |   |   |   |   |--- weights: [850.67, 3543.28] class: 1
|   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |--- weights: [48.46, 1.52] class: 0
|   |--- no_of_special_requests >  0.50
|   |   |--- no_of_special_requests <= 1.50
|   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |--- lead_time <= 102.50
|   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |--- weights: [697.09, 9.11] class: 0
|   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |--- weights: [15.66, 9.11] class: 0
|   |   |   |   |--- lead_time >  102.50
|   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |--- weights: [32.06, 19.74] class: 0
|   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |--- weights: [44.73, 3.04] class: 0
|   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |--- lead_time <= 8.50
|   |   |   |   |   |--- lead_time <= 4.50
|   |   |   |   |   |   |--- weights: [498.03, 44.03] class: 0
|   |   |   |   |   |--- lead_time >  4.50
|   |   |   |   |   |   |--- weights: [258.71, 63.76] class: 0
|   |   |   |   |--- lead_time >  8.50
|   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |--- weights: [2512.51, 1451.32] class: 0
|   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |--- weights: [134.20, 1.52] class: 0
|   |   |--- no_of_special_requests >  1.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |--- weights: [1585.04, 0.00] class: 0
|   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |--- weights: [180.42, 57.69] class: 0
|   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |--- weights: [52.19, 0.00] class: 0
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |--- weights: [184.90, 56.17] class: 0
|   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |--- weights: [106.61, 106.27] class: 0
|   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |--- weights: [67.10, 0.00] class: 0
|--- lead_time >  151.50
|   |--- avg_price_per_room <= 100.04
|   |   |--- no_of_special_requests <= 0.50
|   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |--- lead_time <= 163.50
|   |   |   |   |   |   |--- weights: [3.73, 24.29] class: 1
|   |   |   |   |   |--- lead_time >  163.50
|   |   |   |   |   |   |--- weights: [257.96, 62.24] class: 0
|   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |--- avg_price_per_room <= 2.50
|   |   |   |   |   |   |--- weights: [8.95, 3.04] class: 0
|   |   |   |   |   |--- avg_price_per_room >  2.50
|   |   |   |   |   |   |--- weights: [0.75, 97.16] class: 1
|   |   |   |--- no_of_adults >  1.50
|   |   |   |   |--- avg_price_per_room <= 82.47
|   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |--- weights: [2.98, 282.37] class: 1
|   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |--- weights: [213.97, 385.60] class: 1
|   |   |   |   |--- avg_price_per_room >  82.47
|   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |--- weights: [23.86, 1030.80] class: 1
|   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |--- weights: [5.22, 0.00] class: 0
|   |   |--- no_of_special_requests >  0.50
|   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |--- lead_time <= 180.50
|   |   |   |   |   |--- lead_time <= 159.50
|   |   |   |   |   |   |--- weights: [7.46, 7.59] class: 1
|   |   |   |   |   |--- lead_time >  159.50
|   |   |   |   |   |   |--- weights: [37.28, 4.55] class: 0
|   |   |   |   |--- lead_time >  180.50
|   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |--- weights: [20.13, 212.54] class: 1
|   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |--- weights: [8.95, 0.00] class: 0
|   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |--- weights: [231.12, 110.82] class: 0
|   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |--- weights: [19.38, 34.92] class: 1
|   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |--- lead_time <= 348.50
|   |   |   |   |   |   |--- weights: [106.61, 3.04] class: 0
|   |   |   |   |   |--- lead_time >  348.50
|   |   |   |   |   |   |--- weights: [5.96, 4.55] class: 0
|   |--- avg_price_per_room >  100.04
|   |   |--- arrival_month <= 11.50
|   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |--- weights: [0.00, 3200.19] class: 1
|   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |--- weights: [23.11, 0.00] class: 0
|   |   |--- arrival_month >  11.50
|   |   |   |--- no_of_special_requests <= 0.50
|   |   |   |   |--- weights: [35.04, 0.00] class: 0
|   |   |   |--- no_of_special_requests >  0.50
|   |   |   |   |--- arrival_date <= 24.50
|   |   |   |   |   |--- weights: [3.73, 0.00] class: 0
|   |   |   |   |--- arrival_date >  24.50
|   |   |   |   |   |--- weights: [3.73, 22.77] class: 1


# showing relative importances of features
importances = best_model.feature_importances_
indices = np.argsort(importances)

# graph parameters
plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()


# training performance comparison

models_train_comp_df = pd.concat(
    [
        decision_tree_perf_train_without.T,
        decision_tree_tune_perf_train.T,
        decision_tree_post_perf_train.T,
    ],
    axis=1,
)
models_train_comp_df.columns = [
    "Decision Tree sklearn",
    "Decision Tree (Pre-Pruning)",
    "Decision Tree (Post-Pruning)",
]
print("Training performance comparison:")
models_train_comp_df

Training performance comparison:


# testing performance comparison

models_test_comp_df = pd.concat(
    [
        decision_tree_perf_test_without.T,
        decision_tree_perf_test.T,
        decision_tree_tune_perf_test.T,
        decision_tree_post_test.T,
    ],
    axis=1,
)
models_test_comp_df.columns = [
    "Decision Tree without class_weight",
    "Decision Tree with class_weight",
    "Decision Tree (Pre-Pruning)",
    "Decision Tree (Post-Pruning)",
]
print("Test set performance comparison:")
models_test_comp_df

Test set performance comparison:

	count	mean	std	min	25%	50%	75%	max
no_of_adults	36275.00000	1.84496	0.51871	0.00000	2.00000	2.00000	2.00000	4.00000
no_of_children	36275.00000	0.10528	0.40265	0.00000	0.00000	0.00000	0.00000	10.00000
no_of_weekend_nights	36275.00000	0.81072	0.87064	0.00000	0.00000	1.00000	2.00000	7.00000
no_of_week_nights	36275.00000	2.20430	1.41090	0.00000	1.00000	2.00000	3.00000	17.00000
required_car_parking_space	36275.00000	0.03099	0.17328	0.00000	0.00000	0.00000	0.00000	1.00000
lead_time	36275.00000	85.23256	85.93082	0.00000	17.00000	57.00000	126.00000	443.00000
arrival_year	36275.00000	2017.82043	0.38384	2017.00000	2018.00000	2018.00000	2018.00000	2018.00000
arrival_month	36275.00000	7.42365	3.06989	1.00000	5.00000	8.00000	10.00000	12.00000
arrival_date	36275.00000	15.59700	8.74045	1.00000	8.00000	16.00000	23.00000	31.00000
repeated_guest	36275.00000	0.02564	0.15805	0.00000	0.00000	0.00000	0.00000	1.00000
no_of_previous_cancellations	36275.00000	0.02335	0.36833	0.00000	0.00000	0.00000	0.00000	13.00000
no_of_previous_bookings_not_canceled	36275.00000	0.15341	1.75417	0.00000	0.00000	0.00000	0.00000	58.00000
avg_price_per_room	36275.00000	103.42354	35.08942	0.00000	80.30000	99.45000	120.00000	540.00000
no_of_special_requests	36275.00000	0.61966	0.78624	0.00000	0.00000	0.00000	1.00000	5.00000

	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	required_car_parking_space	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	repeated_guest	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	no_of_special_requests	booking_status
63	1	0	0	1	Meal Plan 1	0	Room_Type 1	2	2017	9	10	Complementary	0	0	0	0.00000	1	0
145	1	0	0	2	Meal Plan 1	0	Room_Type 1	13	2018	6	1	Complementary	1	3	5	0.00000	1	0
209	1	0	0	0	Meal Plan 1	0	Room_Type 1	4	2018	2	27	Complementary	0	0	0	0.00000	1	0
266	1	0	0	2	Meal Plan 1	0	Room_Type 1	1	2017	8	12	Complementary	1	0	1	0.00000	1	0
267	1	0	2	1	Meal Plan 1	0	Room_Type 1	4	2017	8	23	Complementary	0	0	0	0.00000	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
35983	1	0	0	1	Meal Plan 1	0	Room_Type 7	0	2018	6	7	Complementary	1	4	17	0.00000	1	0
36080	1	0	1	1	Meal Plan 1	0	Room_Type 7	0	2018	3	21	Complementary	1	3	15	0.00000	1	0
36114	1	0	0	1	Meal Plan 1	0	Room_Type 1	1	2018	3	2	Online	0	0	0	0.00000	0	0
36217	2	0	2	1	Meal Plan 1	0	Room_Type 2	3	2017	8	9	Online	0	0	0	0.00000	2	0
36250	1	0	0	2	Meal Plan 2	0	Room_Type 1	6	2017	12	10	Online	0	0	0	0.00000	0	0

	Odds	Change_odd%
const	0.00000	-100.00000
no_of_adults	1.11491	11.49096
no_of_children	1.16546	16.54593
no_of_weekend_nights	1.11470	11.46966
no_of_week_nights	1.04258	4.25841
required_car_parking_space	0.20296	-79.70395
lead_time	1.01583	1.58331
arrival_year	1.57195	57.19508
arrival_month	0.95839	-4.16120
repeated_guest	0.06478	-93.52180
no_of_previous_cancellations	1.25712	25.71181
avg_price_per_room	1.01937	1.93684
no_of_special_requests	0.22996	-77.00374
type_of_meal_plan_Meal Plan 2	1.17846	17.84641
type_of_meal_plan_Not Selected	1.33109	33.10947
room_type_reserved_Room_Type 2	0.70104	-29.89588
room_type_reserved_Room_Type 4	0.75364	-24.63551
room_type_reserved_Room_Type 5	0.47885	-52.11548
room_type_reserved_Room_Type 6	0.37977	-62.02290
room_type_reserved_Room_Type 7	0.23827	-76.17294
market_segment_type_Corporate	0.45326	-54.67373
market_segment_type_Offline	0.16773	-83.22724

	Logistic Regression sklearn	Logistic Regression-0.38 Threshold	Logistic Regression-0.42 Threshold
Accuracy	0.80545	0.79265	0.80132
Recall	0.63267	0.73622	0.69939
Precision	0.73907	0.66808	0.69797
F1	0.68174	0.70049	0.69868

	Logistic Regression-default Threshold	Logistic Regression-0.37 Threshold	Logistic Regression-0.42 Threshold
Accuracy	0.80545	0.79265	0.80132
Recall	0.63267	0.73622	0.69939
Precision	0.73907	0.66808	0.69797
F1	0.68174	0.70049	0.69868

Context¶

Objective¶

Data Description¶

Univariate Analysis¶

Bivariate Analysis¶

Outlier Check¶

Model Building¶

Data Preparation for Modeling¶

Building Logistic Regression Model¶

FURTHER EDA¶

Dealing with Multicollinearity¶

Dropping high p-values.¶

Converting coefficient to odds¶

Performance on training set¶

ROC-AUC¶

Improving the model by changing threshold value for AUC-ROC curve.¶

Using the Precision-Recall to check for better optimal threshold¶

Model performance on Training set¶

Comparing training performance of different thresholds¶

Performance on test data¶

Using default threshold value¶

ROC curve on test set¶

Using a model with threshold = 0.38¶

Decision Tree¶

Building Decision Tree Model¶

Model performance on test set.¶

Decision Tree (with class_weights)¶

Important features¶

Pre-pruning¶

Performance on test data¶

Visualizing the decision tree¶

F1 Score vs alpha for training and testing sets¶

Checking performance on training set¶

Checking performance on test set¶

Decision Tree diagram¶

Comparing Decision Tree models¶

Conclusions and Business Recommendations¶

	Booking_ID	no_of_adults	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	avg_price_per_room	no_of_special_requests	booking_status
0	INN00001	2	1	2	Meal Plan 1	Room_Type 1	224	2017	10	2	Offline	65.00000	0	Not_Canceled
1	INN00002	2	2	3	Not Selected	Room_Type 1	5	2018	11	6	Online	106.68000	1	Not_Canceled
2	INN00003	1	2	1	Meal Plan 1	Room_Type 1	1	2018	2	28	Online	60.00000	0	Canceled
3	INN00004	2	0	2	Meal Plan 1	Room_Type 1	211	2018	5	20	Online	100.00000	0	Canceled
4	INN00005	2	1	1	Not Selected	Room_Type 1	48	2018	4	11	Online	94.50000	0	Canceled

	Booking_ID	no_of_adults	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	avg_price_per_room	no_of_special_requests	booking_status
36270	INN36271	3	2	6	Meal Plan 1	Room_Type 4	85	2018	8	3	Online	167.80000	1	Not_Canceled
36271	INN36272	2	1	3	Meal Plan 1	Room_Type 1	228	2018	10	17	Online	90.95000	2	Canceled
36272	INN36273	2	2	6	Meal Plan 1	Room_Type 1	148	2018	7	1	Online	98.39000	2	Not_Canceled
36273	INN36274	2	0	3	Not Selected	Room_Type 1	63	2018	4	21	Online	94.50000	0	Canceled
36274	INN36275	2	1	2	Meal Plan 1	Room_Type 1	207	2018	12	30	Offline	161.67000	0	Not_Canceled

	feature	VIF
0	const	39497686.20788
1	no_of_adults	1.35113
2	no_of_children	2.09358
3	no_of_weekend_nights	1.06948
4	no_of_week_nights	1.09571
5	required_car_parking_space	1.03997
6	lead_time	1.39517
7	arrival_year	1.43190
8	arrival_month	1.27633
9	arrival_date	1.00679
10	repeated_guest	1.78358
11	no_of_previous_cancellations	1.39569
12	no_of_previous_bookings_not_canceled	1.65200
13	avg_price_per_room	2.06860
14	no_of_special_requests	1.24798
15	type_of_meal_plan_Meal Plan 2	1.27328
16	type_of_meal_plan_Meal Plan 3	1.02526
17	type_of_meal_plan_Not Selected	1.27306
18	room_type_reserved_Room_Type 2	1.10595
19	room_type_reserved_Room_Type 3	1.00330
20	room_type_reserved_Room_Type 4	1.36361
21	room_type_reserved_Room_Type 5	1.02800
22	room_type_reserved_Room_Type 6	2.05614
23	room_type_reserved_Room_Type 7	1.11816
24	market_segment_type_Complementary	4.50276
25	market_segment_type_Corporate	16.92829
26	market_segment_type_Offline	64.11564
27	market_segment_type_Online	71.18026

	Logistic Regression-default Threshold	Logistic Regression-0.37 Threshold	Logistic Regression-0.42 Threshold
Accuracy	0.79555	0.79555	0.80345
Recall	0.73964	0.73964	0.70358
Precision	0.66573	0.66573	0.69353
F1	0.70074	0.70074	0.69852

	no_of_adults	no_of_weekend_nights	no_of_week_nights	lead_time	arrival_year	arrival_month	arrival_date	avg_price_per_room	no_of_special_requests	type_of_meal_plan_Not Selected	room_type_reserved_Room_Type 2	market_segment_type_Offline	market_segment_type_Online
13662	1	0	1	163	2018	10	15	115.00000	0	0	0	1	0
26641	2	0	3	113	2018	3	31	78.15000	1	0	1	0	1
17835	2	2	3	359	2018	10	14	78.00000	1	0	0	1	0
21485	2	0	3	136	2018	6	29	85.50000	0	1	0	0	1
5670	2	1	2	21	2018	8	15	151.00000	0	0	0	0	1

	ccp_alphas	impurities
0	0.00000	0.00838
1	0.00000	0.00838
2	0.00000	0.00838
3	0.00000	0.00838
4	0.00000	0.00838
...	...	...
1839	0.00890	0.32806
1840	0.00980	0.33786
1841	0.01272	0.35058
1842	0.03412	0.41882
1843	0.08118	0.50000

	Decision Tree sklearn	Decision Tree (Pre-Pruning)	Decision Tree (Post-Pruning)
Accuracy	0.99421	0.83097	0.89954
Recall	0.98661	0.78608	0.90303
Precision	0.99578	0.72425	0.81274
F1	0.99117	0.75390	0.85551

	Decision Tree without class_weight	Decision Tree with class_weight	Decision Tree (Pre-Pruning)	Decision Tree (Post-Pruning)
Accuracy	0.87118	0.86621	0.83497	0.86879
Recall	0.81175	0.80494	0.78336	0.85576
Precision	0.79461	0.78663	0.72758	0.76614
F1	0.80309	0.79568	0.75444	0.80848